This invention relates to a computing system in which loads can be distributed among different processors/nodes and more specifically relates to maintaining a balance of loads of the nodes, especially where those nodes may have different speeds and mix of applications.
Computing systems employing a plurality of processors/nodes include telecommunications systems and general computing systems where the demands of the applications require processing at multiple nodes. In such systems a software program may be run at multiple nodes to increase its capacity and speed of execution as well as providing reliability associated with redundancy. When a software program or task is distributed to more than one node, managing the load of each node supporting distributed tasks becomes a more complex consideration as compared with the use only one node to completely support a software program or task. Load management is further complicated since the load at a node supporting distributed processing may vary substantially over time as the demands associated with the supported processes increase and decrease. If an existing node becomes unavailable or if a new node is added to the system, the decrease or increase in total processing capacity of the system changes and must be accounted for by a change in the distribution of loads.
One factor influencing the management of loads is the difference in processing speed at each node. For example, a first node may process a task three times faster than a second node. Some currently utilized load management methods assigned a weighting factor corresponding to the speed of execution to each node to accommodate load variations that take into account different processor speeds. In this example to achieve an equal load (measured as the percent of time required to complete the tasks) on the first and second nodes, the first node will be assigned three times the amount of work to process as the second node. The use of such weighting factors permits the load management to accommodate different processing speeds at the nodes. However, such load management techniques are not flexible since each specific node must have a weighting factor assigned. The substitution of a node that has a substantially decreased or increased speed of execution for the node that it replaces will result in load management problems unless the corresponding weighting factor is adjusted. Such adjustments are normally manually made and result in administration work with its associated possibility of human error especially if node substitutions are often made.
Another example in a telecommunication system is mixing different small applications in a powerful processor. This is a cost saving option for a small office with only small capacity needs. The system designer needs redundancies to achieve a higher reliability, i.e., running applications on several independent nodes. However, the system cost will pressure the number of nodes to be minimized. The solution is mixing several applications in one node. If a node cannot take all applications because of some constraints, the mixing of different applications will create a heterogeneous configuration. The need to intelligently balance loads among assigned nodes become a challenge. Each assigned node may have different CPU speeds and different other applications. Therefore, there exists a need for improved load management that will provide increased flexibility in accounting for nodes with different speeds of execution and changes in processing loads.